Klinik und Poliklinik für Innere Medizin II, Klinikum rechts der Isar, Munich, Germany.
III. Medizinische Klinik, Universitätsklinikum Augsburg, Augsburg, Germany.
Crit Care. 2023 May 26;27(1):201. doi: 10.1186/s13054-023-04426-5.
A quantitative assessment of pulmonary edema is important because the clinical severity can range from mild impairment to life threatening. A quantitative surrogate measure, although invasive, for pulmonary edema is the extravascular lung water index (EVLWI) extracted from the transpulmonary thermodilution (TPTD). Severity of edema from chest X-rays, to date is based on the subjective classification of radiologists. In this work, we use machine learning to quantitatively predict the severity of pulmonary edema from chest radiography.
We retrospectively included 471 X-rays from 431 patients who underwent chest radiography and TPTD measurement within 24 h at our intensive care unit. The EVLWI extracted from the TPTD was used as a quantitative measure for pulmonary edema. We used a deep learning approach and binned the data into two, three, four and five classes increasing the resolution of the EVLWI prediction from the X-rays.
The accuracy, area under the receiver operating characteristic curve (AUROC) and Mathews correlation coefficient (MCC) in the binary classification models (EVLWI < 15, ≥ 15) were 0.93 (accuracy), 0.98 (AUROC) and 0.86(MCC). In the three multiclass models, the accuracy ranged between 0.90 and 0.95, the AUROC between 0.97 and 0.99 and the MCC between 0.86 and 0.92.
Deep learning can quantify pulmonary edema as measured by EVLWI with high accuracy.
肺水肿的定量评估很重要，因为其临床严重程度可以从轻度损害到危及生命不等。肺水肿的一种定量替代测量方法是经肺温度稀释（TPTD）提取的血管外肺水指数（EVLWI），尽管具有侵入性，但它可以反映肺水肿的严重程度。迄今为止，X 射线对水肿严重程度的评估仍然基于放射科医生的主观分类。在这项工作中，我们使用机器学习从胸部 X 射线定量预测肺水肿的严重程度。
我们回顾性纳入了 431 例在重症监护病房 24 小时内接受胸部 X 射线和 TPTD 测量的患者的 471 张 X 射线片。从 TPTD 中提取 EVLWI 作为肺水肿的定量测量指标。我们使用深度学习方法，将数据分为两类、三类、四类和五类，从而提高从 X 射线预测 EVLWI 的分辨率。
在 EVLWI<15 和 EVLWI≥15 的二分类模型中，准确性、接受者操作特征曲线下的面积（AUROC）和马修斯相关系数（MCC）分别为 0.93（准确性）、0.98（AUROC）和 0.86（MCC）。在三类多分类模型中，准确性在 0.90 到 0.95 之间，AUROC 在 0.97 到 0.99 之间，MCC 在 0.86 到 0.92 之间。
深度学习可以通过 EVLWI 对肺水肿进行定量评估，具有很高的准确性。
